cellrank.tl.estimators.CFLARE

class cellrank.tl.estimators.CFLARE(object, **kwargs)

Compute the initial/terminal states of a Markov chain via spectral heuristics.

This estimator uses the left eigenvectors of the transition matrix to filter to a set of recurrent cells and the right eigenvectors to cluster this set of cells into discrete groups.

Parameters

• object (Union[AnnData, ndarray, spmatrix, KernelExpression]) –

  Can be one of the following types:

  • anndata.AnnData - annotated data object.

  • scipy.sparse.spmatrix, numpy.ndarray - row-normalized transition matrix.

  • cellrank.tl.kernels.KernelExpression - kernel expression.

  • str - key in anndata.AnnData.obsp where the transition matrix is stored. adata must be provided in this case.

  • bool - directionality of the transition matrix that will be used to infer its storage location. If None, the directionality will be determined automatically. adata must be provided in this case.

• kwargs (Any) – Keyword arguments for cellrank.tl.kernels.PrecomputedKernel.

Attributes

absorption_probabilities	Absorption probabilities.
absorption_times	Mean and variance of the time until absorption.
adata	Annotated data object.
backward	Direction of kernel.
eigendecomposition	Eigendecomposition of transition_matrix.
kernel	Underlying kernel expression.
lineage_drivers	Potential lineage drivers.
params	Estimator parameters.
priming_degree	Priming degree.
shape	Shape of the kernel.
terminal_states	Categorical annotation of terminal states.
terminal_states_probabilities	Aggregated probability of cells to be in terminal states.
transition_matrix	Transition matrix of kernel.

Methods

compute_absorption_probabilities([keys, ...])	Compute absorption probabilities.
compute_eigendecomposition([k, which, ...])	Compute eigendecomposition of transition_matrix.
compute_lineage_drivers([lineages, method, ...])	Compute driver genes per lineage.
compute_lineage_priming([method, early_cells])	Compute the degree of lineage priming.
compute_terminal_states(*args, **kwargs)	Compute terminal states of the process.
copy(*[, deep])	Return a copy of self.
fit([k])	Prepare self for terminal states prediction.
from_adata(adata, obsp_key)	Deserialize self from anndata.AnnData.
plot_absorption_probabilities([states, ...])	Plot continuous or categorical observations in an embedding or along pseudotime.
plot_lineage_drivers(lineage[, n_genes, ...])	Plot lineage drivers discovered by compute_lineage_drivers().
plot_lineage_drivers_correlation(lineage_x, ...)	Show scatter plot of gene-correlations between two lineages.
plot_spectrum([n, real_only, show_eigengap, ...])	Plot the top eigenvalues in real or complex plane.
plot_terminal_states([states, color, ...])	Plot continuous or categorical observations in an embedding or along pseudotime.
predict([use, percentile, method, ...])	Find approximate recurrent classes of the Markov chain.
read(fname[, adata, copy])	Deserialize self from a file.
rename_terminal_states(new_names)	Rename categories in terminal_states.
set_terminal_states(labels[, cluster_key, ...])	Manually define terminal states.
to_adata([keep, copy])	Serialize self to anndata.Anndata.
write(fname[, write_adata, ext])	Serialize self to a file.

Examples

Compute potential lineage drivers